inception模块代码pytorch
时间: 2023-04-24 15:01:00 浏览: 256
Inception v3 迁移学习完整代码
以下是一个简单的Inception模块的PyTorch代码示例:
```python
import torch
import torch.nn as nn
class InceptionModule(nn.Module):
def __init__(self, in_channels, out_channels):
super(InceptionModule, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels // 4, kernel_size=1)
self.conv2 = nn.Conv2d(in_channels, out_channels // 4, kernel_size=1)
self.conv3 = nn.Conv2d(in_channels, out_channels // 4, kernel_size=1)
self.conv4 = nn.Conv2d(in_channels, out_channels // 4, kernel_size=1)
self.conv5 = nn.Conv2d(out_channels // 4, out_channels, kernel_size=3, padding=1)
self.conv6 = nn.Conv2d(out_channels // 4, out_channels, kernel_size=5, padding=2)
self.pool = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)
def forward(self, x):
out1 = self.conv1(x)
out1 = nn.ReLU()(out1)
out2 = self.conv2(x)
out2 = nn.ReLU()(out2)
out2 = self.conv5(out2)
out2 = nn.ReLU()(out2)
out3 = self.conv3(x)
out3 = nn.ReLU()(out3)
out3 = self.conv6(out3)
out3 = nn.ReLU()(out3)
out4 = self.pool(x)
out4 = self.conv4(out4)
out4 = nn.ReLU()(out4)
out = torch.cat((out1, out2, out3, out4), dim=1)
return out
```
这个Inception模块包含四个分支,分别使用不同的卷积核大小和池化操作来提取特征。最后,这些特征被拼接在一起,形成输出特征图。这个模块可以被用于构建更大的神经网络,例如GoogleNet。
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